… of machine-learning (ML) in predicting the outcomes of patients … develop risk scores using
conventional methods and ML to … carcinoma development after hepatitis C virus eradication. …

… the performance accuracy, and predicting the hepatitis C with effective models. The ML …
the UCI Machine Learning repository of Egyptian patients 66 who underwent treatment dosages …

… , this study combines machine learning with SHAP interpretable algorithms, based on …
machine learning model for predicting the treatment response of intermediate-stage HCC patients …

… learning algorithms to predict the HCC disease with highest performance. To the best our
knowledge, we apply the proposed solution (2level genetic optimizer) to detect the HCC for …

… in predicting the response of unresectable HCC to lenvatinib. We used various machine
learning methods and … CT (CECT) data for predicting the response to lenvatinib. These models …

… Classification is a classic data mining task, with roots in machine learning. Associative
classification aims to detect relationships between categorical variables and large datasets. This …

… classification predictions from routine … Hepatitis B virus (HBV) and Hepatitis C virus (HCV)
cases versus HBV or HCV immunoassay positive cases. These methods were illustrated using …

… trees using a subset of the same data set. They compared two machine learning algorithms
that … to liver fibrosis and cirrhosis in patients with chronic hepatitis C infection. They used 73 …

… patients (65%) with a median age of 64 years and a past medical history of alcohol intoxication
(65%), viral hepatitis C (… The preliminary model was defined as the best prediction model …

… a machine learning-… predictions of treatment responses in hepatitis C patients. The focus
of this framework is to explore the potential of Machine Learning Techniques (MLT) in predicting…